matrics_calculator.MAE

Functions

mean_absolute_error(y_true, y_pred)

Calculate the Mean Absolute Error (MAE) metric for regression.

Module Contents

matrics_calculator.MAE.mean_absolute_error(y_true, y_pred)[source]

Calculate the Mean Absolute Error (MAE) metric for regression.

This function computes the average absolute difference between the predicted values (y_pred) and the actual values (y_true). It measures the magnitude of errors in prediction, providing a straightforward evaluation of a model’s accuracy.

Parameters:

y_truearray-like: True values of the target variable.
y_predarray-like: Predicted values from the model.

Returns:

float: The Mean Absolute Error.

Notes:

MAE is defined as:: MAE = (1 / n) * sum(|y_true - y_pred|)

where n is the number of observations.

Examples:

>>> y_true = [100, 200, 300]
>>> y_pred = [110, 190, 290]
>>> mean_absolute_error(y_true, y_pred)
10.0